Application of XGBoost in Gas Infrared Spectral Recognition

Mengqi Tao; Jiaxiang Liu; Yue Wu; Zhiqiang Ning; Yonghua Fang

doi:10.3788/AOS202040.0730002

[1] Sheehe S L, Jackson S I. Identification of species from visible and near-infrared spectral emission of a nitromethane-air diffusion flame[J]. Journal of Molecular Spectroscopy, 364, 111185(2019).

[2] Han Y Z, Zhang Y X, Chang S J et al. Recognition for the nonlinear fluorescence spectra based on optimal wavelet transform and artificial neural network[J]. Journal of Optoelectronics·Laser, 16, 718-721(2005).

[3] Bai P, Xie W J, Liu J H. Method of infrared spectrum analysis of hydrocarbon mixed gas based on multilevel and SVM-subset[J]. Spectroscopy and Spectral Analysis, 28, 299-302(2008).

[4] Bai P, Wang J H, Wang H K et al. A method of mixed gas component infrared spectrum recognition based on SVM regression model[J]. Acta Photonica Sinica, 37, 754-757(2008).

[5] Liu M J, Feng W W, Shi F R et al. Fast algorithm for feature extraction and identification of infrared spectra of polluted gases[J]. Spectroscopy and Spectral Analysis, 26, 1854-1857(2006).

[6] Yu D H. Research on gas recognition and concentration detection algorithm based on infrared spectrum[D]. Chengdu: University of Electronic Science and Technology of China, 13-58(2018).

[7] Chen T Q, Guestrin C. XGBoost: a scalable tree boosting system. [C]∥Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 13-17, 2016, San Francisco, California. New York: ACM, 785-794(2016).

[8] Zopluoglu C. Detecting examinees with item preknowledge in large-scale testing using extreme gradient boosting (XGBoost)[J]. Educational and Psychological Measurement, 79, 931-961(2019).

[9] Torlay L, Perrone-Bertolotti M, Thomas E et al. Machine learning: XGBoost analysis of language networks to classify patients with epilepsy[J]. Brain Informatics, 4, 159-169(2017).

[10] Li D Z, Wang C, Li Y Y. Evaluation of fan blade icing based on XGBoost algorithm[J]. Electric Power Science and Engineering, 35, 43-48(2019).

[11] Zhang X, Luo A. XGBOOST based stellar spectral classification and quantized feature[J]. Spectroscopy and Spectral Analysis, 39, 3292-3296(2019).

[12] Zhang W W, Liu D, Jia X Y. Three classified coupon prediction based on XGBoost algorithm[J]. Journal of Nanjing University of Aeronautics & Astronautics, 51, 643-651(2019).

[13] Mo H, Sun H J, Liu J J et al. Developing window behavior models for residential buildings using XGBoost algorithm[J]. Energy and Buildings, 205, 109564(2019).

[14] Wang X, Wang X, Lu S L, Lü S L, Li Y, Li Y et al. Automatic baseline correction of gas spectra based on baseline drift model[J]. Spectroscopy and Spectral Analysis, 38, 3946-3951(2018).

et alAutomatic baseline correction of gas spectra based on baseline drift model[J]. Spectroscopy and Spectral Analysis, 38, 300-305(2018).

[15] Liu J, Koenig J L. A new baseline correction algorithm using objective criteria[J]. Applied Spectroscopy, 41, 447-449(1987).

[16] Zhao Y S, Xue X M, Song X J et al. Comparison and analysis of FT-IR spectra for six broad-leaved wood species[J]. Journal of Forestry Engineering, 33, 40-45(2019).

[17] Yang S Q, Yan L J, Liu N et al. Asphalt index based on characteristic spectral analysis of infrared spectrum[J]. Journal of Jiangsu University(Natural Science Edition), 40, 244-248(2019).

[18] Zhuang L, Song X J, Xu Y H. Study on the infrared spectral characteristic of tetracentron sinense wood[J]. Hubei Agricultural Sciences, 56, 1334-1339, 1344(2017).